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arXiv:2603.19635v1 Announce Type: new Abstract: The exponential expansion of context windows in LLMs has unlocked capabilities for long-document understanding but introduced severe bottlenecks in inference …
arXiv:2603.19294v1 Announce Type: new Abstract: While post-training has successfully improved large language models (LLMs) across a variety of domains, these gains heavily rely on human-labeled …
arXiv:2603.19295v1 Announce Type: new Abstract: Mental disorder populations exhibit pronounced heterogeneity -- that is, the significant differences between samples -- poses a significant challenge to …
arXiv:2603.19296v1 Announce Type: new Abstract: To tackle the huge computational demand of large foundation models, activation-aware compression techniques without retraining have been introduced. However, since …
arXiv:2603.19297v1 Announce Type: new Abstract: The static knowledge representations of large language models (LLMs) inevitably become outdated or incorrect over time. While model-editing techniques offer …
arXiv:2603.19298v1 Announce Type: new Abstract: Operator situation awareness is a pivotal yet elusive determinant of human reliability in complex nuclear control environments. Existing assessment methods, …
arXiv:2603.19299v1 Announce Type: new Abstract: In recent years, progress in medical informatics and machine learning has been accelerated by the availability of openly accessible benchmark …
arXiv:2603.19302v1 Announce Type: new Abstract: Machine unlearning is increasingly important for clinical language models, where privacy regulations and institutional policies may require removing sensitive information …
arXiv:2603.19307v1 Announce Type: new Abstract: Modeling the complex interactions among functional subnetworks is crucial for the diagnosis of mental disorders and the identification of functional …
arXiv:2603.19308v1 Announce Type: new Abstract: In autonomous driving, multi-agent collaborative perception enhances sensing capabilities by enabling agents to share perceptual data. A key challenge lies …
arXiv:2603.19310v1 Announce Type: new Abstract: Training large language models (LLMs) for complex reasoning via reinforcement learning requires reward labels that specify whether the generated rollouts …
arXiv:2603.19312v1 Announce Type: new Abstract: Joint Embedding Predictive Architectures (JEPAs) offer a compelling framework for learning world models in compact latent spaces, yet existing methods …
